17 lines
No EOL
1.2 KiB
TeX
17 lines
No EOL
1.2 KiB
TeX
% Abstract
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\abstract{
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Networks are versatile objects able to represent various types of
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interactions and bipartite networks are particularly useful in ecological
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context for interaction between different entities (e.g. plant-pollinator). As the networks grow in size, reliable metrics, models and methods are needed to detect structure and perform analysis.
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Those methods exist and are pretty robust for single network analyses
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but we have motivation to consider a collection of network,
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in order to compare their structure or partition them.
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For collection of simple networks a colSBM (collection Stochastic Block Model~\cite{chabert-liddellLearningCommonStructures2024a}) has been proposed.
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We adapt this model to the bipartite case with a variational
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Expectation-Maximization algorithm for inference, a clever parameter space
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exploration and a BIC-like criterion for model selection. Building on this
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method we present a partitioning algorithm to gather networks based
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on their shared structures. We perform simulation studies to assess
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performance of our models and algorithm. Finally, we apply our clustering
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algorithm on ecological networks.
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} |